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Download by: [Missouri S & T] Date: 21 June 2016, At: 12:33

Application of discrete event simulation in optimising coal mine room-and-pillar panel width: a case study Angelina Anani1*, Kwame Awuah-Offei2 and Joseph Hirschi3 A key design aspect of room-and-pillar coal mines is the panel width (or number of entries in a panel), which affects unit mining costs and productivity. Traditional mine design approaches do not facilitate optimisation of unit mining costs and productivity as a function of the panel width. Discrete event simulation can be used to facilitate optimal panel width selection that minimises unit mining costs and maximises productivity. The objective of this study is to evaluate the impact of panel width on the cost and productivity of a room-and-pillar operation using discrete event simulation. The mining system is modelled as a discrete event model that estimates unit costs and productivity for a given panel widthDownloaded by [Missouri S & T] at 12:33 21 June 2016

primarily on ventilation requirements, haulage distances (fleet Room-and-pillar (R&P) mining is one of the oldest self-­ requirements), geotechnical conditions and property boundaries supported underground mining methods used for exploiting (Dunrud 1998; Zipf 2001; Loui and Sheorey 2002, Ghasemi flat and tabular deposits such as coal. One of the main goals of and Shahriar, 2012). However, it is important that panel width R&P mine design is to extract the maximum possible amount determination takes into account minimising operating costs, of ore while maintaining the bearing rock’s strength and con- optimising cut sequences, and maximising overall productivity. dition. Design parameters in R&P mining depend on several Panel dimensions are determined by the number of pil- factors, including depth of mining, production recovery, sta- lars and rooms in the panel. Panel width is defined by the bility of the hanging wall and coal strength (Farmer 1992). A number of entries (production and development) excavated key aspect of R&P mine design is panel design. Panel design in the panel. Due to advances in geotechnical methods over relies on pillar strength and dimensions within the panel, as the past few decades, varying designs with increasing panel well as coal recovery and production requirements. widths have been implemented (Robson 1984; Cain 1999). Paneling in R&P mining is done to divide the mine into Common strategies employed in R&P panel development different areas for ventilation and other aspects of mine oper- include: (i) developing a base width (typically seven or more ation. Barrier pillars are used to separate panels thus pre- entries) on advance in increments (measured in crosscuts) that venting progressive collapse in case of pillar failure within a correspond with required belt and power moves, (ii) mining panel. The number of barrier pillars needed to separate panels rooms on either side or both sides of the base width and (iii) depends on the number of panels being planned. As panel recovering pillars on retreat, if that practice is allowed. width increases, so does barrier pillar size. Panel dimensions Typically, the importance of panel design is emphasised also affect the mining (cut) sequence with wider panels result- in retreat mining where entries and rooms are mined first and ing in more complicated cut sequences and more tramming by then panel pillars are mined afterwards. Pillar recovery is continuous miners (CMs), a necessary function during which common in metal mines where high grade ore cannot be left coal is not produced. Consequently, panel width (number of behind. Even though panel recovery in coal mines is not prev- entries) affects the overall cost and production recovery of an alent, recent advances in electric haulage units have spurred operation. An efficient R&P mine design takes into account a move towards wider panels to take full advantage of their the optimum panel width that maximises production and min- capabilities. However, the effect of wider panels on produc- imises cost while ensuring bearing rock stability. tivity and unit operating costs has not been fully investigated. Traditional design approaches do not facilitate optimisation 1 Mining and Nuclear Engineering, Missouri University of Science and of unit mining costs and productivity as a function of panel Technology, Rolla, MO 65401, USA 2 Mining and Nuclear Engineering, Missouri University of Science and width (Zipf 2001; Loui and Sheorey 2002; Ghasemi and Technology, Rolla, MO, USA Shahriar 2012). The objective of this study was to evaluate 3 Mining and Mineral Resources Engineering, Southern Illinois University, the impact of panel width (number of entries) on the cost and Carbondale, IL, USA productivity of R&P operations. The authors present R&P *Corresponding author, email akakc2@mst.edu

panel width design as a possible dual objective optimisation of uncertainty on the performance indicator. Similarly, Petering problem where we seek to maximise productivity and mini- (2009) investigated the optimal width of storage blocks in a mise unit costs subject to all constraints. The approach is to terminal container and its effect on gross crane rate. build a discrete event simulation (DES) model and use it to The advantage of DES lies in its ability to model complex conduct experiments to estimate the productivity and unit systems with relative ease. DES allows for implementation of costs at varying panel widths and operating conditions. As a new designs and methods without interfering with the real-life case study, a coal mine is modelled as a DES using Arena® system. DES also helps answer the question of why certain (Rockwell Automation Inc., Milwaukee, WI). phenomenon occur (Asplund and Jakobsson 2011). It has the ability to capture random behaviour caused by a large number of factors that impact the system, using the Monte-Carlo sim- Discrete event simulation for panel ulation technique. This gives a sense of interactions between width optimisation variables that make up such systems. DES can be used to perform ‘bottleneck’ evaluations to DES for engineering design ­discover where work in process in a system is delayed and DES is a computer-based approach that facilitates modelling, which variables affect this. Identifying problems and gaining simulation and analysis of the behaviour of complex systems understanding into the importance of these variables increases as a sequence of discrete events. DES software has been con- awareness of their importance relative to the performance of the tinuously improved for the past four decades leading to more overall system. DES allows an analyst to vary the system oper- advanced simulation languages (Nance 1995; Pegden et al. ating periods, cheaply and easily (Schriber 1977). On the other 1995; Rice et al. 2005). Simulation languages are symbols/ hand, even though DES provides a way to analyse and under- codes recognised by computers or computer programs as stand the changing behaviour of the system, it only provides an issued commands a programmer wishes to perform (Kiviat estimate of the model output. Building DES models can be costly 1969). Common simulation languages currently used for DES and time consuming. Therefore, it is used on an as-needed basis include SIMAN (system modelling), GPSS and SLAM. This where benefits outweigh costs (Asplund and Jakobsson 2011). work uses Arena®, which is based on the SIMAN languageDownloaded by [Missouri S & T] at 12:33 21 June 2016

This study seeks to apply DES to facilitate better panel width

for DES modelling and simulation. design. The general concept is presented in Section DES for SIMAN is a SIMulation ANalysis program generally used to panel width design model either discrete, continuous or a combination of discrete and continuous systems (Pegden et al. 1995). SIMAN allows process-oriented, event-oriented and continuous components DES for panel width design to be integrated into a single system. A unique characteristic of a SIMAN program is the distinct decomposition of model Ultimately, optimising a design parameter is an optimisation and experimental frames. The static or dynamic nature of a sys- problem as described by Equation (1). The decision variable, tem can be defined in the system model. Different experiments vector x, represents variables that affect the objective func- can be done in the experimental framework resulting in mul- tion, f (x). Possible values that these variables can take make tiple sets of output (McHaney 1991). However, the close link up the set of feasible solutions (alternative designs). between its arithmetic and list processes on the one hand and minimise f (x) its demand-resource concepts on the other restrict its capability (1) subject to x ∈ Ω to model demand-driven systems (Fishman 2001). Applications of discrete event simulation as a In the case of panel width design, the objective function could ­decision-making tool for improve mining systems are vast reflect the desire to maximise mining recovery and produc- and increasing (Doe and Griffin 1987; Sturgul and Harrison tivity as well as minimise unit operating costs. This work 1987; Harrison and Sturgul 1988; Basu and Baafi 1999; focuses on the dual objective of maximising productivity and Vagenas 1999; Awuah-Offei et al. 2003; Michalakopoulos minimising unit costs. Decision variables can be panel width, et al. 2005; Yuriy and Vayenas 2008; Ben-Awuah et al. cut sequences, the number of CMs and the number of haulage 2010). However, DES application in R&P mining is limited units assigned to each CM. to a few examples (Hanson and Selim 1974; Pereira et al. If the objective function can be written mathematically 2010). None of them deal with using DES to determine (explicit) in terms of the decision variables and all constraints can optimal design parameters. Hanson and Selim (1974) used be described similarly, there are many techniques to solve such event based models to compare room and pillar mining optimisation problems. Often, however, the objective function is with long wall mining systems. Pereira et al. (2010) used highly non-linear and implicit. In such cases, very few techniques discrete event simulation to maximise the coal faces extrac- (simulation being one) can solve the problem (Kleijnen 1998). tion from a given panel. It still remains that DES applica- In the case of panel width optimisation, productivity and tion specifically in room and pillar mines is limited even unit costs associated with production functions of cutting, more so in optimising design parameters. loading and hauling as a function of the panel width, equip- DES can be used as a decision-making tool in determining ment fleet and cut sequence are non-linear and implicit. DES optimum design parameters. Since DES can be used to simu- offers a means to estimate the unit cost and productivity for late system performance at varying operating conditions and a given panel width, equipment fleet and cut sequence. The design parameters, what-if analysis can be performed quickly approach taken in this work is to first build a valid DES model and cheaply with a valid model. Through such experiments, of coal loading and hauling operations, then determine a feasi- optimum design parameters can be determined that meet design ble set of decision variable values (panel widths, fleet and cut goals and respect all constraints of the design problem. For sequences) and estimate all productivity values and unit costs instance, to design a greenhouse crop system for maximum for these conditions using the model. The optimal solution production and quality of labour, Van’t Ooster et al. (2013) suc- can then be selected on the basis of the objective function. cessfully used DES to perform sensitivity analysis in identify- To find the optimal solution, one would have to determine ing parameters that influence labour performance and the effect the relative significance of productivity and unit cost to the

2 Mining Technology 2016

Case study A case study of an actual coal mine is presented in this section to illustrate the approach discussed in Section DES for panel width design. The discussion here follows the general steps of a typical simulation study (Kelton et al. 2003).

Problem formulation The objective of the panel width optimisation study is to eval- uate the impact of panel width on the unit cost and produc- tivity of an underground R&P operation. A DES model with variables that characterise the mining system was built using 1 Haulage unit dumping time Arena®. The model predicts unit mining costs and produc- tivity at different panel widths. The DES model was validated with shift production data obtained from a R&P coal mine in southern Illinois, United States of America (USA). The defined performance metric was that the relevant simulated output should be within 15% of actual values from the mine.

System and simulation specifications

The mine used for this study is located in southern Illinois, USA. The mine produces approximately 7 million tons ofDownloaded by [Missouri S & T] at 12:33 21 June 2016

coal per year from the Herrin No. 6 seam using R&P mining methods with a panel recovery rate of 54%. Eight Joy Model 14CM27 CMs (two for each panel) cut and load coal at up to 40 tons per minute with a maximum cutting height of 11.2 feet. 2 Empty haulage unit travel speed Coal is hauled from CMs to feeder-breakers by 20-ton Joy Model BH20 battery-powered haulage units. A ­feeder-breaker is located at the centre of each production panel to transfer mined coal from haulage units to conveyor belts. As the panel advances, it is moved forward in three-crosscut increments with each crosscut forming a row of in-line pillars across the panel. The full width of the panel is mined in six-cross-cut increments. The mine has experimented with different panel widths and mining sequences. Currently, advancing base widths of 11 and 13 entries before mining rooms is the most common. Maximum and minimum panel widths are 21 and 11 entries, respectively. Each CM mines up to seven entries on one side of a panel. The objective of the simulation is to develop a valid DES model that predicts unit mining costs and productivity and 3 Loaded haulage unit travel speed provides basic animation for verification. Input data used in the model were obtained from time studies done at the mine as described by histograms in Figs. 1–6. Raw data were analysed to fit statistical distributions using the chi-squared goodness- of-fit test as shown in Table 1. Input data include loading and dumping times, payloads and battery change data, which are sampled from the distribution. Model output includes produc- tion per shift, tons per hour, total operating costs including equipment costs and the calculated cost per ton for a given panel width.

Model formulation: CM and haulage logic

The DES modelling framework requires system entities, resources and processes to be specified by the analyst. To 4 Loaded haulage unit travel time initiate modelling, entities go through defined processes in a logical manner waiting for needed resources to become available at each process (i.e. resources are ‘busy’ if they are decision. Since this varies from one situation to another, the being used by other entities) before they go through the pro- authors chose not to attempt finding a single optimal solution, cess. The CM is modelled as a resource used for the loading but to present a discussion of results relative to productivity process and can only load one haulage unit at a time. Loads and unit costs. of coals are modelled as entities with specific attributes (entity

number, payload and cut sequence). Battery-powered haulage

units are model as guided transporters used for hauling loads (entities). A guided transporter is an Arena®-specific mod- elling construct for material haulage (Rockwell Automation Inc. 2012). Transporters use entries and crosscuts as haulage routes, which are modelled to restrict traffic flow such that any point on a haulage route can only accommodate one haulage unit at a time since mine openings are not wide enough for them to pass each other. The feeder-breaker is also modelled as a stationary resource used for dumping loads (entities). The feeder-breaker and each cutting face are modelled as stations. Points in the model where transporters transfer entities are called stations. Haulage routes between stations are mod- 5 Haulage unit spotting time elled as network links to capture varying haulage distances. Distances for each network link are an input to the model. Figure 7 shows the logic used to model the system.

Verification and validation

An animation of the system was designed and used to verify that the model performs as intended. The resource, trans- porters, stations, and network links are modelled as part of the animation for loading and transporting coal (entities). Shift production data from the mine was used to validateDownloaded by [Missouri S & T] at 12:33 21 June 2016

the model. For validation, the simulation model predicted

coal production (load count/shift) and shift duration, which was compared with data from a time-and-motion study con- 6 CM travel time between cuts ducted in one of the sections of the host mine where the panel was being advanced with 13 entries. The time-and-motion study collected data for 18 CM cuts completed over two

7 DES model logic

4 Mining Technology 2016

8 Cut sequence for 11-entry base width

non-consecutive shifts. During the first shift, 6.33 hours were as an input based on mining practices at the mine. Mining spent making 11 cuts with the remaining time spent on con- faces in the 11- or 13-entry base width are mined using the veyor belt and CM repairs. The second shift was spent entirely cut sequence shown in Fig. 8. Rooms are mined using theDownloaded by [Missouri S & T] at 12:33 21 June 2016

on production; however, data was collected for only the first cut sequence shown in Fig. 9. The experiment evaluates a half of the shift during which seven cuts were completed. super-section mining system with two CMs (one on each side For both shifts, coal was hauled by four haulage units with of the section). The conveyor belt is located in the centre an average payload of 10,886 kg (12 tons). The production entry of the panel. reported from the CM’s onboard monitoring system indicates The simulation output includes production data (e.g. load that the mine produced 2.2M kg (2448 tons) of coal from 204 count and total production tonnage), duration of mining and loads and 3.2M kg (3576) from 298 loads during the first shift percentage of time the CM spends loading haulers. Other and second shift, respectively. outputs include total cost of mining and estimated unit costs In the validation experiment, 150 replications were con- (Equation (2)). Results of simulation experiments are dis- ducted to obtain estimates of load count and total coal pro- cussed in the Results and Discussion section. duction, mining duration and other output. The number of replications selected was such that the half-width1 of the min- nCM CCM + nH CH tr + CF ( ) ing duration (the most uncertain output) is less than 1% of the Unit costs($∕ton) = 2 Total production estimated duration. The cut sequence used in the validation experiment duplicated that used during the time-and-motion where nCM and nH are the number of CMs and haulage units, study. Each replication stops when all specified cuts have been respectively; tr is the duration of the simulation run; CCM and mined in the simulation. Results are discussed in the Results CH are hourly costs for CMs and haulage units, respectively; and Discussion section. and CF is fixed costs, which include labour and equipment for advancing belt and power systems.

Simulation experiments and analysis

Results and discussion Simulation experiments were designed to analyse the effect of panel width (number of entries), number of haulage units Validation assigned to each CM, and the cut sequence on mining cost Table 2 shows the results of the validation experiments for the and productivity. At the mine, the staff have experimented first production shift. The model takes a bit longer (30 min with cut sequences that advance 11 or 13 entries first before more) to mine the 11 cuts and also loads 24 more haulage units expanding into rooms, if necessary. Hence, these experiments than the observed system. The key performance measures are were to evaluate whether to advance with 11 or 13 entries the number of loads mined from the 11 cuts and the duration, before mining rooms. Once the initial advance is mined, which are within 11 and 8%, respectively, of the actual values. the mine has mined anywhere from no additional rooms to Both values are within the 15% specified earlier. The model five additional rooms on each side. Hence, the experiment was thus deemed valid and used for all the experiments. includes three factors: • Number of base width entries (11 or 13); Effect of panel width • Number of rooms (0, 1, …, 5 rooms on each side of the panel); and Figures 10–17 show simulation results for experiments with • Number of haulage units assigned to each CM (3, 4, the default number of haulage units (four per CM) where 11- or 5). and 13-entry systems are mined with 0, 1, 2, 3, 4 or 5 rooms This leads to a total of 2 × 6 × 3 = 36 combinations of on each side for a total number of entries between 11 (11-entry experiments. For each experiment, 150 replications were run system with 0 rooms on each side) and 23 (13-entry system for the analysis. Each replication was run until all cuts in the with 5 rooms on each side). These results indicate the effect of sequence have been mined. The cut sequence was provided panel width (number of entries) on productivity and unit costs.

11 entry system 13 entry system

Figures 10 and 11 show that total production and duration 100,000 of mining increase with increasing number of entries. This Producton (tons)

is what one would expect, if the model is performing well.

Figures 12 and 13 show that the percentage of production 50,000 time the CM spends loading haulage units initially increases with increasing panel width until an optimal panel width. This 0 indicates that there is excess haulage unit capacity in the sys- tem with less than optimal number of entries. CM operations 11 13 15 17 19 21 23 are inefficient due to the excessive spotting time resulting Number of entries in long wait times and bunching; however, expanding panel width beyond the optimal results in inadequate haulage unit 10 Total production capacity and under utilisation of the CM. This is confirmed by Figs. 14 and 15 showing that the optimal panel width. Initial expansion of the panel reduces the haulage unit cycle 11 entry system 13 entry system time (minimises waiting time). However, further expansion 240 of the panel increases haulage unit cycle times because haul distances become longer, leading to a haulage unit constrained Duration (hrs)

operation. Adding more haulage units will increase produc-

140 tivity and CM utilisation as discussed in Section Effect of number of haulage units. These trends (cycle time and CM loading times) 40 directly result in the observed trend in productivity (Fig. 16 11 13 15 17 19 21 23 Productivity). Panel widths of 17 and 19 entries result in max- Number of entries imum productivity when advancing with a base width of 11 entries and 13 entries, respectively. However, this trend is not 11 Duration of mining mirrored in unit cost results (Fig. 17) due to the effect of fixed costs that make larger panels more cost-effective even with sub-optimal productivity. In Fig. 17, unit costs are estimated 11 entry system 13 entry system using Equation (2). Hourly costs of haulage units and CM 25.5 are estimated at $79.13 and $122.40 (InfoMine 2013).2 Fixed Loading time (%)

costs for moving the belt are estimated at $81,050.

25.0 The following observations can be made from these results: 24.5 • 11-entry systems outperform 13-entry systems under similar conditions (cut sequences and equipment); 24.0 • Haulage unit cycle times correlate very well with pro- 11 13 15 17 19 21 23 ductivity and CM loading; Number of entries • There appears to be an optimal panel width for a given number of haulage units based on productivity analysis; 12 CM time spent loading (LHS) and

Number of entries 520

18 Effect of number of haulage units on productivity for

11 entry system 13 entry system 11-entry system 10.55 10.50 Cyle time (mins)

however, the increase when the number of haulage units

10.45 increases from three to four is much more significant than 10.40 the increase when the number of haulage units increases from 10.35 four to five. Also, the number of haulage units can affect 10.30 optimal panel width. For example, Fig. 18 shows that optimal 10.25 panel width with three haulage units assigned to each CM is 10.20 11 13 15 17 19 21 23 13 entries, whereas with four haulage units, optimal panel width is 17 entries. This is because the number of assigned Number of entries haulage units affects the width at which the system becomes limited by haulage unit capacity. 15 Average cycle times (RHS) Figures 20 and 21 show the sensitivity of unit cost results to number of haulage units. With each additional haulage 11 entry system 13 entry system unit, unit costs increase for both 11- and 13-entry systems. The following observations can be made based on these 560 results: Productivity (tph)

16 Productivity Effect of fixed costs From Equation (2), if fixed costs are negligible, the unit costs • Unit costs decrease with increasing number of entries curve should be the inverse of the productivity relationship. due to the effect of fixed costs. However, Figs. 16 and 17 do not show this relationship indicating that fixed costs significantly affect the unit cost Effect of number of haulage units relationship. Figure 22 shows the sensitivity of the unit cost relationship to fixed costs using results for the 11-entry system Figures 18 and 19 show the sensitivity of productivity results with four haulage units (same as Fig. 17). Figure 22 shows to the number of haulage units. It can be observed that with that the unit cost relationship will indeed show an optimal at the addition of each haulage unit, productivity increases; 17 entries if fixed costs are less than or equal to $1000. Fixed

3 Cars 4 Cars 5 Cars costs as low as $2000 more than compensate for any decline in productivity due to under-resourced CMs. That is, with high 560 fixed costs (≥$2000), unit costs for mining larger panels will be lower, even though productivity will be sub-optimal after Productivity (tph)

550 the panel width exceeds the optimal panel width for produc- tivity. From a cost perspective, larger panels are advantageous 540 because of fixed costs included in moving belt and power.

530 Conclusion 520 This research effort has successfully built a discrete event 13 15 17 19 21 23 simulation model that can be used to facilitate panel Number of entries width design. The DES model is capable of ­evaluating the effect of panel width (number of entries) on R&P 19 Effect of number of haulage units on productivity for mine ­productivity and unit costs. The DES model has 13-entry system ­successfully been ­validated for the mine that cooperated with this study. The validated model has been used to ­evaluate the effect of panel width on productivity and unit 3 Cars 4 Cars 5 Cars costs of the mine. $3.60 Based on results of this work, the following general con- clusions can be made: Unit costs ($/ton)

$3.20 • For particular operating conditions (equipment, cut

$2.40 • For particular operating conditions, an optimal panel

width exists that minimises unit costs, only if the fixed $2.00 costs are negligible. For any significant fixed cost, larger 11 13 15 17 19 21 panels will always result in lower unit costs. Number of entries For the cooperating mine, in particular, the following con- clusions can be drawn: 20 Effect of number of haulage units on unit costs for • The 11-entry system is better than the 13-entry system. 11-entry system This is a function of cut sequences used. • The practice of moving the belt after mining three cross- cuts to ensure haul distances to rooms is reasonable. 3 Cars 4 Cars 5 Cars • The optimal panel width under simulated conditions is $3.60 17 entries (3 rooms on each side of the 11-entry base width). Unit costs ($/ton)

$3.20 • Four (4) haulage units should be assigned to each CM

in the panel. $2.80

$2.40 Acknowledgement $2.00 13 15 17 19 21 23 This work was made possible with funding from the Illinois Clean Coal Institute. The authors are grateful for the support Number of entries of Prairie State Generating Company, owners of the cooper- 21 Effect of number of haulage units on unit costs for ating mine for their support during this research. The authors 13-entry system are also thankful to Ms Sisi Que and Mr Mark Boateng for their assistance.